Search Results (2,847)

The flying-wing configuration offers inherent advantages in aerodynamic efficiency and stealth; however, conventional fixed-wing designs face fundamental performance trade-offs when tasked with multi-role missions. This paper introduces a multidisciplinary design optimization (MDO) framework for a morphing wing unmanned aerial vehicle (UAV) to overcome this limitation. The proposed UAV integrates four complementary morphing strategies—shear-type variable sweep, variable span, morphing wingtip, and a continuously variable camber trailing edge—to adapt its geometry for different flight phases. An automated parametric modeling platform is developed, enabling the dynamic generation of 3D CAD models driven by design variables. This geometry is coupled with a suite of analysis modules for aerodynamics, propulsion, weight estimation, flight performance, and radar cross-section. The multi-mission profile, including takeoff, climb, cruise, turning, and landing, is decomposed into several phase-specific single-objective optimization subproblems, which are solved using an elitist real-coded genetic algorithm. The results quantify the optimal morphing configurations for each phase, demonstrating significant performance gains over the baseline, such as a 17% increase in range. Critically, the study analyzes the trade-off between aerodynamic benefits and the weight penalty of morphing mechanisms, revealing that both range and maneuverability are the most sensitive to the added weight. The proposed framework uses mission-phase-specific optimum geometries to define the required morphing envelope, actuation ranges, and net performance benefit of a candidate morphing flying-wing UAV after considering mechanism-induced mass penalties. This framework provides a quantitative basis for mission-driven morphing decisions and establishes a viable approach for designing highly adaptive next-generation UAVs. Full article

Aircraft type classification plays a crucial role in various applications, including remote sensing, surveillance, and aviation management. Since the development of deep learning techniques, nearly all related methods are based on neural networks, achieving excellent classification results. However, existing classification networks primarily focus on optimizing single-branch architectures, often overlooking the underlying factors driving recognition performance. Our analysis suggests that color and shape are two important and complementary visual cues for aircraft classification, with their relative importance varying across datasets and imaging scenarios. Motivated by this insight, we propose a novel dual-branch network architecture that separately processes shape and color cues, allowing each branch to emphasize one type of visual information before adaptive fusion. Specifically, we designed two dedicated modules: a Shape Feature Module (SFM) and a Color Feature Module (CFM), tailored for extracting shape and color information independently. Furthermore, we introduced a Color–Shape Cross-Attention-based Fusion Module (CSCAFM) to integrate these features. Within CSCAFM, the separated shape and color features are adaptively fused through a cross-attention mechanism, enabling the network to dynamically weigh the contributions of shape and color. Experimental results on benchmark datasets demonstrate the effectiveness of our approach. Full article

The surging need for public security risk mitigation has placed stricter demands on the modernization of emergency response capacities. Unmanned aircraft systems (UASs) offer a promising solution for liquid anti-riot agent dispersion, yet the complex interaction between rotor-induced downwash and droplet trajectories makes nozzle layout optimization a significant challenge. To address the prohibitive computational costs of traditional Computational Fluid Dynamics (CFD) and the limitations of single-objective optimization, this study proposes an integrated “simulation–modeling–optimization–decision” framework. First, a linear nozzle layout was identified as superior to the traditional circular arrangement, achieving a 44.8% increase in deposition rate. Subsequently, Optimal Latin Hypercube Sampling (OLHS) and CFD simulations were combined to construct high-precision Kriging surrogate models for three key indicators: deposition rate, uniformity, and coverage rate. The NSGA-II algorithm was then employed to solve the multi-objective trade-off, followed by the entropy-weighted TOPSIS method to identify the optimal engineering solution. Results indicate that nozzle count is the dominant system-level variable under the constant per-nozzle flow-rate condition, showing strong positive correlations with all performance indicators. The identified optimal configuration (6 nozzles with a 1.88 m boom length) achieved a 66.1% increase in deposition rate and an 18.7% increase in coverage rate compared to the original circular layout. Furthermore, the surrogate-based framework improved optimization efficiency to 296% compared to full factorial methods. This study provides a scientific theoretical basis and a highly efficient technical pathway for the structural design of high-performance UAV spray systems. Full article

The rapid expansion of Unmanned Aircraft System (UAS) operations has created an urgent need for scalable strategic conflict resolution methods within the U-space framework. When requested 4D flight plans overlap with previously authorised ones, the Flight Authorisation Service (FAS) denies the request, and can provide the UAS operator with an alternative route, free of conflict. This work introduces a Machine Learning-based tool designed to support this process, which consists of three sequential phases. First, an Octree spatial partitioning technique is proposed, discretising the airspace, further identifying the previously occupied cells and visualising the occupied airspace, so that the UAS operator can manually find an alternative route. Then, the widely known A* pathfinding algorithm is implemented in this discretized airspace, allowing the shortest or most optimal conflict-free alternative route. Finally, the methodology integrates a Machine Learning (Reinforcement Learning) model, created from scratch and trained with realistic flight trajectories from a PX4 Simulator, to further optimise flight paths, explicitly accounting for operational constraints such as distance and battery consumption. In this work, both methods are compared, addressing traditional algorithms limitations with Machine Learning (ML) techniques, showing that a near-optimal behaviour can be achieved with the ML approach, at a fraction of the computation time needed. Full article

Heat exchanger integration is a key design consideration for engines adapted to run on hydrogen and requiring liquid hydrogen to be preheated prior to combustion. For a typical small turboshaft, a comparison is made of fuel heating via an intercooler, a recuperator, or both in combination. This steady-state, zero-dimensional thermodynamic assessment examines the overall performance effects of the heat exchanger installations, heat loads and setpoint temperatures. It shows that exhaust gas recuperation provides up to 15% SFC reduction relative to an engine using power offtake for fuel preconditioning, with an average reduction of 14% across the evaluated operating points. Fuel heating via an intercooler is constrained by off-design and low-temperature thermal management requirements, so it only gives modest SFC benefits and will reduce specific power unless the engine is substantially redesigned. Within the evaluated design space, the combined intercooled and recuperated arrangement does not provide the lowest SFC, but it offers a balanced heat load distribution that may help to mitigate the risk of local air-side icing in the heat exchangers. Unlike previous works that considered turbofan engine architectures, this study focuses on turboshaft and turbogenerator installations where shaft power objectives and operating constraints determine the relative merits of alternative heat exchanger integration strategies. It includes an assessment of potential effects on NOx emissions as well as SFC. The study provides guidance for preliminary design and sizing of heat exchangers for fuel thermal management, but analysis of transients in the cryogenic systems and detailed assessments of aircraft-level integration penalties will be specific to particular engine applications and are beyond the scope of the present study. Full article

Focusing on the self-powering demand of aircraft engine bladed disks (blisks), this paper investigates piezoelectric vibration energy-harvesting modeling and non-linear circuit performance. A multi-sector electromechanical coupled model is established to analyze the frequency splitting and vibration localization induced by minor structural mistuning. By breaking the cyclic symmetry, mistuning severely concentrates vibration energy into a specific sector, providing a localized high-energy concentration region for optimal energy extraction. To enhance recovery efficiency and load adaptability, three interface circuit topologies—Standard Energy-Harvesting (SEH), Parallel Synchronized Switch Harvesting on Inductor (P-SSHI), and Double Synchronized Switch Harvesting (D-SSHI)—are comparatively analyzed. Through wideband spatial–spectral dynamic response and steady-state impedance matching analyses, the non-linear energy conversion and transfer mechanisms are systematically characterized. Results demonstrate that synchronized switching circuits significantly improve energy transmission via forced voltage inversion, accompanied by a notable equivalent stiffness enhancement effect induced by electromechanical coupling. Furthermore, the D-SSHI topology not only exhibits substantial advantages in peak power extraction, but also, owing to its internal LC energy decoupling mechanism, forms a broad load-independent power plateau across an extremely wide impedance range. This research provides robust theoretical foundations for designing highly resilient self-powered intelligent blades under extreme operating conditions. Full article

Fixed-wing unmanned aircraft are widely used for aerial mapping because they can acquire high-resolution data at relatively low cost, but maintaining both energy efficiency and image quality in the presence of wind and flight-performance limits remains challenging. In practice, operators introduce buffer regions and extended waypoints outside the area of interest to cope with deviations during turning, which increases flight distance and energy use; yet, this approach can still degrade image overlap near the boundary. This paper presents a path-planning framework that designs turning maneuvers compatible with bank-angle, stall-margin, and roll-rate constraints while aligning mapping lanes directly with the area of interest. The framework combines analytically structured turn patterns, an energy-based metric that accounts for increased aerodynamic load in banked flight, and a two-stage path-angle selection procedure that uses a fast, simplified model to guide a more detailed optimization. Simulation studies on both idealized and real survey geometries indicate that, within the considered maneuver families and assumptions, the proposed method can reduce the integrated aerodynamic energy metric and improve coverage compliance relative to a conventional path-following approach that relies on overshoot points. Full article

During aeromagnetic surveys using fixed-wing aircraft, magnetometers mounted inside the cabin are strongly affected by platform magnetic interferences. Existing aeromagnetic compensation models have difficulty in accurately modeling these complex magnetic interferences and often suffer from limited generalization capability. This paper proposes an intelligent compensation algorithm integrating a dual-fluxgate physical extension with an enhanced attention mechanism. First, an extended Tolles–Lawson (T-L) model leverages physical complementarity between dual fluxgates to enhance interference feature representation. Building on this, a physics-informed dual-branch parallel network architecture is designed. The physical branch dynamically models and decouples linear interference components, while the nonlinear branch introduces an improved attention mechanism to capture and remove non-stationary nonlinear interference in the signals. More importantly, the dual-branch network demonstrates superior generalization in level-flight extrapolation tests. Compared to traditional linear methods and pure data-driven models, the proposed approach reduces the residual standard deviation (STD) to 0.16 nT and achieves an improvement ratio (IR) of 22.31. This research significantly advances aeromagnetic compensation precision, offering a robust and high-performance solution for precision geophysical exploration. Full article

The utilization of boundary layer ingestion in combination with a turbofan engine with an ultra-high bypass ratio is regarded as one of the possible solutions to increase the energy efficiency of the aircraft as a complete system. However, this concept inevitably leads to strong coupling between the external aircraft flow and engine internal flow, associated with an increased degree of flow non-uniformity. As a consequence, the engine components experience changed matching, and their performance is dependent on the engine power settings, aircraft design and flight conditions. All of these installation effects are reflected in engine performance modeling, which can enable reliable engine performance assessment. In this context, this article investigates the sensitivity of such engines and discusses possible approaches and preliminary ideas in extending engine performance modeling. Full article

Insulation failure in aviation generator windings is one of the most common faults. Modern aircraft winding materials often employ polyimide enameled wire, making research on its reliability and health monitoring particularly important. Based on the relationship between temperature and aging rate described by the Arrhenius law, this study designed accelerated thermal aging experiments, testing twisted-pair, coil, and winding samples made of copper-core polyimide enameled wire. The variation in multiple parameters was visualized using B-spline fitting, ultimately identifying parallel equivalent capacitance as the most suitable parameter for monitoring generator winding insulation. It was also indicated that aging of the winding insulation coating has almost no effect on the performance of the electrical system. Finally, experimental data were processed using Bayesian nonlinear regression, where prior data were updated with new data to obtain posterior aging curves. When the IC ($C_p$) value reaches 1.2009 and 1.4089 times its initial value, the sample is considered to have reached 50% and 100% of its lifespan, respectively. This provides a reference approach and quantitative indicators for predicting the lifespan of polyimide enameled wire windings. Full article

Rotary direct-drive pressure valves (RDDPVs) have attracted increasing attention in aircraft braking systems because of their compact structure, reduced weight, and improved anti-contamination capability. However, the pressure regulation of RDDPVs is challenging due to the coupled dynamics among the limited-angle torque motor, eccentric spool motion, steady-state flow force, and load pressure. Existing pressure control methods for RDDPVs are still mainly based on linear controllers such as PI/PID controllers, which do not explicitly compensate for the nonlinear characteristics of the valve. To address this problem, this paper investigates the application of a model-based backstepping control strategy to RDDPV pressure regulation. First, a nonlinear dynamic model of the RDDPV is established, and a nonlinear state-space representation is derived for controller design. Based on this model, a backstepping pressure controller is developed, in which the nonlinear model information is used for feedforward compensation. Dynamic surface control is introduced to avoid direct differentiation of the virtual control signals. Lyapunov analysis shows that the closed-loop tracking errors are uniformly ultimately bounded under bounded disturbances. Comparative simulations are conducted under different reference pressure trajectories and randomized model parameter and disturbance conditions. The simulation results indicate that the proposed controller achieves better tracking performance than the controller without detailed model compensation and the conventional PI controller under the tested operating conditions. This study provides an initial simulation-based exploration of model-based nonlinear pressure control for RDDPVs. Full article

Collaborative Research Centres (CRCs) are research institutions in which researchers from several German universities work together within a multidisciplinary research programme. A large number of projects led by one or several researchers from the participating research institutions characterize them. Integrated Research Training Programmes (IRTGs) can be part of the CRC’s supporting structures. They offer a structured training programme with the aim not only of supporting the doctoral researchers in their research activities but also making an engagement in the CRC attractive to young researchers. Key aims are to promote the doctoral researchers’ academic independence and to enable them to gain further qualifications. The integrated research training group of the CRC SFB-TRR 364 SynTrac-Synergies of Highly Integrated Transport Aircraft is inspired by the principles of New Work. This required an adjusted definition of New Work to fit the vision of the CRC SynTrac and the requirements of today’s highly talented doctoral researchers. On this basis, we designed the physical, inter-personal and virtual work-space and the methods which allow the doctoral researchers to perform the activities they “really, really” want to do. We report on two years of experience with this design of the IRTG. Full article

Aiming at the problems of extremely scarce measured samples and significant domain shift between simulated and measured data in automatic target recognition (ATR) of air targets for spaceborne radar, this paper proposes an inverse synthetic aperture radar (ISAR) image recognition method for air targets combining physics-driven data augmentation guided by detection prior information with domain adversarial transfer learning. First, the mapping relationship between scattering point projection and ISAR images is established by using the target 3D point cloud and radar observation geometric priors, and a 2D sinc kernel function is introduced for energy distribution rendering. Then, under the unsupervised transfer learning paradigm, aiming at the distribution inconsistency between augmented data (source domain) and unlabeled simulated data (target domain), this paper designs a cross-domain recognition task experiment including six types of typical aircraft targets, and compares the cross-domain recognition performance of three transfer learning methods (model fine-tuning, deep domain confusion (DDC) and domain-adversarial neural networks (DANN)) on the target domain. Meanwhile, t-distributed stochastic neighbor embedding (t-SNE) visualization is used to analyze the feature distribution alignment ability of the models. Simulation experiments show that the DANN model with a dynamic inversion coefficient introduced in the gradient reversal layer (GRL) achieves a recognition accuracy of 99.5% on the unlabeled target domain, which is significantly superior to the model fine-tuning and DDC methods. Moreover, it makes the feature distributions of source and target domain samples highly overlapping, and maintains a strong inter-class discriminability while eliminating the domain shift. The proposed scheme provides a physically interpretable and robust technical path for few-shot radar target image recognition. Full article

This paper presents a web-based visual analytics framework for robust multi-objective aircraft wing design. Aerodynamic and structural simulation data are generated for a redesigned CRM wing, with aspect ratio and skin root thickness as key variables. Ordinary Kriging surrogates are coupled with NSGA-III to explore trade-offs among lift-to-drag ratio, wing mass, and range. Input design uncertainties are propagated using Monte Carlo Simulation with Halton sampling, enabling low-cost robustness assessment. An interactive HTML–Python dashboard provides contour plots, sampled design points, and Pareto fronts, allowing engineers to perform what-if analyses and rapidly identify robust Pareto-optimal designs. Results show that a higher aspect ratio with lower skin thickness improves aerodynamic efficiency and range, while structural constraints and uncertainty bounds define feasible regions. The Kriging surrogate achieves a Surrogate Speed-Up Index (SSI) of $\mathcal{O}\left({10}^3\right)$, offering comparable insight into wing mass, range, and $L/D$ at roughly three-orders-of-magnitude-lower computational cost than direct mid-fidelity simulations. Full article

Achieving climate-neutral aviation requires propulsion systems capable of reducing emissions and noise while maintaining high aerodynamic efficiency. Distributed Electric Propulsion (DEP) represents a promising solution; however, accurately predicting the unsteady aerodynamic interactions between multiple propellers and lifting surfaces remains challenging. This work investigates the aerodynamic performance of two Distributed Propulsion (DP) configurations using FLOWUnsteady, a meshless Computational Fluid Dynamics (CFD) solver based on the reformulated Vortex Particle Method (rVPM) within a Large-Eddy Simulation (LES) framework. The Lagrangian particle formulation eliminates mesh generation and limits numerical dissipation. Two layouts—a twin wingtip-mounted arrangement and a four-propeller configuration including inboard units are analyzed and compared with a clean wing baseline as functions of propeller position, inflow speed (20 and 33 m/s), and angle of attack. Beyond global aerodynamic performance metrics, the rVPM–LES framework provides a time-resolved and spatially resolved characterization of local propeller–wing interference in multi-propulsor configurations, highlighting differences in loading and torque demand between inboard and wingtip propellers that are not typically captured by low- to mid-fidelity modeling approaches. The results show that distributed propulsion increases lift and reduces drag relative to the clean wing by accelerating the local flow, delaying separation, and enhancing wing circulation. Thrust and torque coefficients exhibit a clear dependence on rotational speed and angle of attack: inboard propellers experience stronger aerodynamic interference and higher torque demand, whereas wingtip propellers maintain more uniform loading. These findings confirm the capability of the meshless rVPM approach to accurately and efficiently capture unsteady interactions in distributed propulsion systems, supporting its application to the analysis and design of future DEP aircraft. Full article